What could an oscillating training loss curve represent?
Try lowering the learning rate.

Such a loss curve can be indicative of a high learning rate. Due to a high learning rate the algorithm can take large steps in the direction of the gradient and miss the local minima. Then it will try to come back to the minima in the next step and overshoot it again. 

You may also try switching to a momentum-based GD algorithm. Such a training loss curve can be indicative of a loss contour like in this example, for which momentum-based GD methods are helpful.

I noticed that you have a very small training set. You may have better luck with a larger training data (~1000 examples) or using a pre-trained Conv network as a starting point.